What does "stationary" mean in the context of reinforcement learning?
A stationary policy is a policy that does not change. Although strictly that is a time-dependent issue, that is not what the distinction refers to in reinforcement learning. It generally means that the policy is not being updated by a learning algorithm.

If you are working with a stationary policy in reinforcement learning (RL), typically that is because you are trying to learn its value function. Many RL techniques - including Monte Carlo, Temporal Difference, Dynamic Programming - can be used to evaluate a given policy, as well as used to search for a better or optimal policy.

Stationary dynamics refers to the environment, and is an assumption that the rules of the environment do not change over time. The rules of the environment are often represented as an MDP model, which consists of all the state transition probabilities and reward distributions. Reinforcement learning algorithms that work online can usually cope and adjust policies to match non-stationary environments, provided the changes do not happen too often, or enough learning/exploring time is allowed between more radical changes. Most RL algorithms have at least some online component, it is also important to keep exploring non-optimal actions in environments with this trait (in order to spot when they may become optimal).

Stationary data is not a RL-specific term, but also relates to the need for an online algorithm, or at least plans for discarding older data and re-training existing models over time. You might have non-stationary data in any ML, including supervised learning - prediction problems that work with data about people and their behaviour often have this issue as population norms change over timescales of months and years.